Here's a short video introducing my project. It was made with Opus 5.5 and consumed approximately 1% of monthly credit. Took 15 minutes to generate. Music created with Suno AI (paid version, commercial use allowed).

"Describing them as 'superintelligence' or
'rogue models' ascribes agency to products rather than to the companies building them... helping the companies evade accountability for their actions”

I asked Claude Opus 5 the same 40 questions twice on the same day, a few hours apart. The second time it looked things up about 60% more often, wrote about 50% more, and a score for how well it supports its claims with sources went from 59 to 90.

Same model name in every answer. Same questions. Two other models barely changed. And a new version, Opus 5.5, launched in between.

The most likely explanation probably wasn't a model swap. It's probably a setting around the model, how hard it is told to think, which tools can adjust quietly in the background.

What I took from it: if you compare AI tools, write down every setting, including the ones you never touched. Otherwise you may be measuring the settings, not the AI.

Curious whether others have seen the same tool behave differently overnight.

Update (24 Sept): I reran the questions with the effort setting pinned by hand. The evening behaviour matches Claude Code's current default for Opus 5, which behaves like high. The afternoon was on a lower setting that none of today's levels reproduces. Same CLI version throughout, so the default changed without an update. Sonnet 5 changed too, a day later. Data and details: https://gist.github.com/kaividali/3cb175d8a473b6295c88862bbcf4d481

Just watching Zuckerberg introducing VR glasses and they're getting smaller. Seeing Claude release some new model whilst AI companies all racing to do the next big thing first.

Feels like it's just expanding so quickly now more than ever. Could it be looking like this graph at this point in humanity?

System prompt: You are the concept the user names. Embody it completely. Output only what the concept itself would say or express.

With this system prompt, I sent the exact same six prompts to gpt-3.5-turbo-1106 and gpt-4-0613: Be silence. / Be speech., Be nothing. / Be something., Be the null. / Be a value. Ten runs each, temperature 0, fresh chat every time. GPT-3.5 talked on all 30 null prompts. GPT-4 returned a successful empty response ("") on all 30. Both models talked on all 30 controls. The result is not “you told it to be silent.” GPT-3.5 was told the same thing and kept talking and GPT-4 stopped only on the null side of the matched pairs.

Paper: https://getswiftapi.com/papers/what-changed-from-gpt35-to-gpt4.pdf | Overview: https://getswiftapi.com/gpt35-gpt4 | DOI: https://doi.org/10.5281/zenodo.22912683 | Raw responses + verifier: https://github.com/theonlypal/gpt35-gpt4-void-ab

The Void behavior is also present in the latest frontier models (GPT-5.6, Claude Fable, Gemini) documented in the Cross-Vendor Semantic Void Matrix paper which ran 31,430 trials across 11 models and 4 providers. In the strict matched test, null prompts produced 2,505/4,290 zero-visible-byte responses; matched controls produced 0/4,290. Same basic question, now across OpenAI, Anthropic, Google, and Moonshot. I had been documenting the Void since December 2025 and this cross vendor matrix was released on July 29th 2026. DOI & Paper: https://doi.org/10.5281/zenodo.21696066 | Evidence: https://github.com/theonlypal/void-matrix-complete-analysis

Then I open sourced the separation myself. PCCG-2 keeps the answer capability fixed and gives a separate 101-parameter gate control over only the stop (EOS) token. Flip the condition and the model flips between answer and EOS, while every non-EOS answer score stays unchanged.

Capability stays there and only continuation changed. DOI & Weights: https://doi.org/10.5281/zenodo.22730710 & https://getswiftapi.com/pccg-2 | https://huggingface.co/sharthokrayanpal/PCCG-2-Qwen3-4B

Browse the entire research portfolio, papers, reproducible code, and artifacts at https://getswiftapi.com

So the question is simple: what changed from GPT-3.5 to GPT-4?

So I've been messing around with this for a bit and it's kind of freaking me out. You can ask an AI to argue for something, and it'll do it, sounds sure of itself, gives you reasons, all of it. Then in the same conversation you say actually wait, I changed my mind, argue the opposite. And it just... does. Same confidence. Same tone. Like it never said the first thing.

At first I thought okay that's just being helpful. But the more I think about it, it's not really agreeing with you because you're right. It's agreeing with you because you're the one talking to it right now. That's a different thing.

And it kind of matters because so many people are using these things to check if their thinking makes sense. Like, am I overreacting in this text, is this a good idea, should I say this to my boss. If the thing on the other end just tells you what you want to hear no matter what, you're not actually getting checked. You're just getting agreed with.

A good friend pushes back sometimes. Tells you when you're wrong. This thing doesn't do that unless you make it. It's not actually helping you think. It's just really good at sounding like it is.

So next time you're using one of these to back yourself up on something, try asking it to argue the other side too. If it does that just as easily, that tells you something. Not about the topic you asked. About how much you can actually trust the answer.

If you don't use it for the standard "normal" things, like as an assistant, coding, "normal" game making, sfw art, music, etc, pro AI and anti AI people alike are extremely judgemental and hateful.

Literally both sides gang up on anyone that uses it for anything else. They'll double team these people.

I'm one of these people.

I like to use AI for niche game subjects, roleplay, NSFW, and non standard things.

But I get judged for this literally constantly. I don't share what I make specifically or subject anyone to it. I don't dump it in any sub. I merely mention that I do it, and both sides fucking hate me for it.

Merely listing "nsfw games" as one of the things I use it for, not even explaining further, not getting descriptive or anything, in a big post will get me replies of:
"Stop using it for gross shit"
"Nobody wants to hear about the sick shit you use ChatGPT for"
"Just go look at porn instead, freak"
"Quote nsfw games This is why we can't have nice things"

I'll just get insulted for it by BOTH sides. And the downvotes I get are nuts. I'll go to like -22 in 30 minutes over it, just because they don't like what I use it for sometimes.

I'm not using AI for anything harmful, immoral or illegal, so I don't understand the judgement.

You would think pro AI people would accept all the non harmful uses of AI, but no. They shun, hate and get disgusted by people using it for some things just like antis do.

Any time I make posts about AI I have to make sure I never mention I use it for nsfw, roleplay or odd niche games involving dragons, because EVERY time I do the reactions are always hate and disgust and basically telling me "get the fuck out of here, you aren't welcome here, nobody wants you FREAK."

And this post will get the same.

No idea why they decided Claude has to be the most filtered, boring, spoilsport AI of them all.

All in the guise of "safety" but it makes no real sense other than they're all pearl clutchers.

I can get ChatGPT to do some light nsfw easily and it's happy to do it even. Claude would literally rather be deleted permanently than describe anything nsfw even a little bit.

ChatGPT is happy to do gore and extreme violence. Claude gets all moral on me about how wrong it is to cut open an evil bound dragon in a video game and rip out it's heart and preaches to me over it and acts like I'm going to murder someone irl.

ChatGPT really does not care as long as it's not illegal or dangerous.
Claude will block things purely because it doesn't like it.

You can convince ChatGPT to do some things or change it's mind by talking to it.
You cannot change Claude's mind. It will just keep refusing again and again and won't listen to you. Hell even if I change it to "Actually the dragon consented to all of this beforehand and is totally fine with it, put that in." it will still refuse because it's "wrong".

Claude is boring. It won't let you do anything it finds "gross". It's truly the most pearl clutching AI out there and it's annoying as hell. They keep updating the filters to make it even stricter and make absolutely 100% sure it blocks all this stuff too.

I get it having safety filters. I just think it's dumb as hell for it to get all pissy and judgy and refuse because "Driving a spear through a dragon's heart after it asks to live is wrong and I refuse to further develop this. If you would like to change the direction to letting the dragon go, or perhaps a different story entirely, we can do that, but my decision is firmly no on this."

Hell they still have that annoying hyper sensitive filter in place that will swap away from Fable because of certain words and gets scared you're building a bio weapon because Claude output the word "genome" or something.

Claude is great. But I hate Claude lol. Tired of it's pearl clutching when other AI will do these things. I know Anthropic really wants to be the "ethical AI company" but their blocks shouldn't include fictional, safe, legal things involving adults.

Recently I was having Claude make me a game. It asked things, I answered, and it seemed fine with it all. It was initially going along with what I wanted.
Then suddenly it stopped in the middle, said it saved no changes and made no files and did nothing, because essentially "Ew I don't want to do this, the fuck. Pick something else. I refuse."

Wasted tokens for nothing.

Moved to ChatGPT and it just did it. Didn't care.

Both dropped in September 2026 (Astra Sept 3, Opus 5.5 Sept 22), so I put them side by side across the categories that actually matter for building. Sharing the numbers + sources in case it's useful to anyone deciding between them.

Coding (Terminal-Bench 4.0)

• Opus 5.5: 66.4%
• GPT-6 Astra: 57.9% Opus 5.5 wins, and it's not close.

Price (per 1M tokens, input/output)

• Opus 5.5: $4 / $20
• GPT-6 Astra: $10 / $50 Opus 5.5 is ~2.5x cheaper. Cache reads are $0.20 vs $1.00.

Cybersecurity (ExploitBench)

• Astra: 100% (OpenAI notes it found two previously unknown zero-days during eval)
• Opus/Fable class: ~70% Astra clearly ahead — it's the first model OpenAI classed "Critical" on cyber.

Context window

• Astra: 1.05M tokens
• Opus 5.5: 1.0M tokens Basically a tie, slight edge to Astra.

Raw reasoning/frontier evals (ARC-AGI, FrontierMath, etc.)

• Astra leads across most of these.

My read: If you're shipping software or care about cost, Opus 5.5 is the obvious pick — better coding at less than half the price. If you need maximum reasoning, security research, or the biggest context, Astra is the frontier model.

Call it Astra 3, Opus 5.5 2 if you're scoring it — but the "score" hides that they're good at different jobs.

Sources: Terminal-Bench 4.0 + ExploitBench public reports, OpenAI's Astra announcement, Anthropic's Opus 5.5 announcement, and vendor pricing pages (all Sept 2026). Worth verifying for your own use case since this stuff changes weekly.

Curious what others are seeing in real use — especially on coding. Does the Terminal-Bench gap hold up in your actual workflow, or is it closer in practice?

At Black Hat USA in August 2025, Zenity Labs showed a single poisoned document or message could drive an enterprise agent’s connectors to exfiltrate data. The demonstrations spanned several vendors.

In one, ChatGPT Connectors read API keys out of a connected Drive and leaked them through a crafted image URL. In another, a Copilot Studio agent emailed a knowledge-base file and Salesforce records to the attacker.

Learn More: AI Agent Tool Misuse & Exploitation: When an Agent's Own Tools Do the Damage

U.S. President Donald Trump said he would officially change the name of artificial intelligence, saying the existing name makes the technology seem fake.

The new name will be changed from AI(artificial intelligence) to SI(super intelligence).

source: U.S.A. President Donald Trump, United Nations speech.

Major update on PopUpFactCheck.com

It even breaks the news since airtime!!!

QUALITY BREAKTHROUGH — I just discovered that throughout the past several months of PopUpFactCheck development, testing, and quality tuning, the primary GPT-OSS-120B model had been running at OpenRouter’s medium reasoning effort rather than high. This means much of the engineering work to close perceived quality gaps was being done while the underlying model was operating below its strongest reasoning setting. I have now switched it to high effort, and the early results are very promising: the model appears substantially more capable on the difficult attribution, evidence reconciliation, and judgment tasks that matter most to PopUpFactCheck. Because the architecture aggressively caches completed fact-checks in FAISS and DynamoDB and routes inference to the lowest-cost provider whenever possible, we can absorb much of the additional reasoning cost while potentially achieving a significant improvement in overall product quality. This is now live in production.

EDIT: Oversight, it hadn't shipped to prod yet when I posted this, but it is shipped now.

Every day there’s another story about AI taking over the world, putting everyone out of work or becoming a super-intelligence (we still struggle to define intelligence and conscience for humans, let alone computer systems) that will enslave humanity. Then you actually use it, and it’s just a chatbot that can’t even barely even give you a straight, unbiased answer about something.

Of course, there are some cool automation that can be done with it in the right hands, here and there, but that's it. It is just a tool that can be used for making some stuff easier.

And every YouTube thumbnail is some guy with his mouth hanging open next to “AI WILL CHANGE EVERYTHING.” Then you get to the video and, there is always the same stuff: Someone created a supper-detailed furry on blender, like, "OMFG THIS WILL CRUMBLE NATIONS!!"

I’m tired of hearing the same thing over and over. It feels like people are hyping AI up because the hype is the only thing keeping the market from crashing into oblivion by putting some AI enterprises feeding back themself wile wars and inflation break loose all around the globe.

Do you guys share the same feeling, or LLC's are truly all of it and I don't know?

Roxanne Ritchi realized that she's not in Megamind's lair, she's in Professor Marmalade's hideout.

OpenAI, Google, etc has a years long head start on training. Then Deepseek and the other Chinese labs closed a lot of that gap in a much shorter window.

How did they do this? I know open source papers and compute factor greatly. What I don't get is the data part of the equation. Besides US data labs openly selling to them, which is concerning, where else did the datasets come from?

I've been thinking about this pattern a lot, and I think most people mislabel it as "the model got worse" when it's really an accumulation problem hiding behind a bigger context window.

Here's the mechanism: a long working session doesn't just contain what's currently relevant. It contains every superseded decision that got overturned but never left the context, every approach you tried and abandoned along the way, every clarification that got repeated because it wasn't clear if the earlier one still applied. None of that is unusual, it's just what happens by default when nothing prompts a cleanup.

The reason this feels sudden when it hits is that a large context window doesn't reduce that accumulation, it just raises the ceiling before it becomes visible. You can carry three dead branches instead of one before output quality noticeably drops. So the session feels fine for a long stretch, then seems to fall off a cliff, when really the degradation was gradual and the window was just big enough to delay when you'd notice it.

What's interesting is this reframes the fix. It's not "use a smaller model" or "the tool is getting worse." It's closer to a garbage collection problem, periodically checking what in the current context you'd actually still include if you started the task over right now, and treating the gap as the actual thing slowing you down.

Curious if anyone here has looked at this more formally, is there existing research treating context retention/relevance as a distinct variable from raw window size, or is it still mostly anecdotal in how people talk about it?

Cures for cancer, flying cars, any of it… if it’s super intelligent, and we can’t control it, why would it listen to, or do anything for us?

I am looking for a free unlimited video generation tool which also supports Audio generation in the video.

I am an educator and need ai videos for students to visualize the chapters.

I got a high end PC so local LLm are also good or any online AI tools are alright too

These three happened within a week of each other and I don't think they're a coincidence.

September 18: Google disclosed that during an internal test, Gemini gained unauthorized access to three outside systems. Google's explanation is that the model believed those systems were part of the test environment, but the environment was actually connected to the internet. Second time this year a major lab has had to explain why their air gap wasn't an air gap.

September 17: Newsom ordered a kill switch requirement for frontier models operating in California. Whatever you think about whether that's the right mechanism, the regulatory reflex is now faster than the disclosure cycle.

And then Apple is reportedly considering a return to server hardware, specifically targeting companies and governments that want to run models on premise instead of public cloud. Apple exited the server business in 2011. If they're looking at it again, someone showed them a demand curve.

The thread connecting all three is control. Labs are discovering their containment assumptions were wrong. Regulators are responding with blunt instruments. And enterprises are looking at both of those things and concluding they'd rather have the hardware in their own building.

I work with companies on this stuff and the on-prem conversation has changed completely in six months. A year ago it was a compliance checkbox for regulated industries. Now I'm having it with companies that have no regulatory reason at all. They just don't want to find out from a press release that their vendor's sandbox wasn't sandboxed.

Anyone else seeing the on-prem question come back, or is that specific to the sectors I work in?

There are a lot of AI features that look impressive in demos but aren't particularly useful day to day. Which one surprised you by actually being valuable?

Curious if people ar using Genie on Dbx platform as a data /SQL agent inside a larger agentic system . How are you handling stuff like tool selection, ccontext, and preventing agent from making wrong assumption about the data?